AI news archive.
19,861 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIagentsWhat the Author Wrote, What the Agent Reads: Silent Cue Loss in Enterprise Document Ingestion
arXiv:2610.30026v1 Announce Type: cross Abstract: Enterprise agents act on documents. Before an agent can follow a policy, answer a compliance question or file a ticket, some ingestion layer must turn a Word file or PDF…
- arXiv cs.AIresearchSP-HLM: Sparse-Pose-Driven Motion Generation and Whole-Body Control for Humanoid Loco-Manipulation
arXiv:2610.30044v1 Announce Type: cross Abstract: Humanoid loco-manipulation requires an effective interface between high-level task policies and whole-body control. Existing interfaces typically rely on a single action…
- arXiv cs.AIagentsSWE-DIVE: Multi-Turn Coding Agent Evaluation with Dynamic User Simulation
arXiv:2610.30047v1 Announce Type: cross Abstract: Coding agents are typically evaluated using one-shot, well-formulated prompts. Benchmarks such as SWE-bench use existing GitHub issues as input for a coding agent and eva…
- arXiv cs.AIresearchFrom Reasoning to Action: Benchmarking Frontier AI Models on LIBERO-10 Robot Manipulation Tasks
arXiv:2610.30051v1 Announce Type: cross Abstract: Recent advances in frontier AI models have demonstrated remarkable capabilities in visual understanding, reasoning, planning, and tool use, raising the possibility of usi…
- arXiv cs.AIagentsParallel AI Agent Collaboration as Graph-Structured Distributed Block Optimization
arXiv:2610.30074v1 Announce Type: cross Abstract: Parallel AI agents can produce useful local edits yet degrade a shared solution when their edits are combined. We model this coordination problem as discrete block optimi…
- arXiv cs.AIresearchAtomicRL: Atomic Reinforcement Learning for Long-Horizon Robotic Manipulation
arXiv:2610.30097v1 Announce Type: cross Abstract: Vision-language-action (VLA) models have shown promise in robotic manipulation, but imitation learning (IL) alone often struggles to master complex long-horizon skills. R…
- arXiv cs.AIresearchDifferentially Private Sparse Reward Estimation with Preference Feedback
arXiv:2610.30099v1 Announce Type: cross Abstract: Reinforcement Learning with Human Feedback (RLHF) has become a central paradigm for aligning Large Language Models with human values. However, the reliance on sensitive p…
- arXiv cs.AIresearchBatchBench: Benchmarking AI Robustness to Batch Effects and Domain Shift in Biomedical Images - A JUMP Cell Painting Leaderboard
arXiv:2610.30104v1 Announce Type: cross Abstract: Despite recent advances, current AI systems remain highly susceptible to domain shifts, particularly in biomedical imaging, where images are usually acquired under substa…
- arXiv cs.AIagentsAsymmetric On-Policy Distillation Learns In-Context Exploration
arXiv:2610.30105v1 Announce Type: cross Abstract: Sequential decision-making under partial observability is difficult because agents must explore to infer hidden, task-relevant information before acting effectively. Lear…
- arXiv cs.AIagentsMultimodal Embodied Agents: A Unified Capability-centric Perspective from Computer-Use to Robot-Use
arXiv:2610.30108v1 Announce Type: cross Abstract: Multimodal agents (MMAs) sustain interaction through reasoning, memory, tools, and feedback, most visibly as computer-use agents, while robotic systems (RSs) couple sensi…
- arXiv cs.AIagentsQuant-Arena: Benchmarking LLM Agents for Quantitative Trading Research
arXiv:2610.30113v1 Announce Type: cross Abstract: Quantitative research is an iterative loop: a researcher proposes a market hypothesis, implements it as an executable signal, tests it on historical data, and decides wha…
- arXiv cs.AIresearchThe End of Consensus Research?
arXiv:2610.30114v1 Announce Type: cross Abstract: Consensus is a fundamental problem in distributed computing and at the core of state machine replication (SMR). Despite decades of active human research, discovering prac…
- arXiv cs.AIresearchEnvironment Duels: A Microcosm of LLM Co-Evolution
arXiv:2610.30116v1 Announce Type: cross Abstract: Through a tangle of real-world interactions, from synthetic training pipelines to data shared on the open web, frontier LLMs absorb what their predecessors and competitor…
- arXiv cs.AIresearchPhysiCalWorld: In-Context Physics Calibration for World Models
arXiv:2610.30125v1 Announce Type: cross Abstract: Existing world models typically perceive a scene through current visual frame or a narrow history window. However, these observations may not reveal the physics propertie…
- arXiv cs.AIresearchThink Harder Where It Matters: Item-Level Adaptive Recursion for Sequential Recommendation
arXiv:2610.30128v1 Announce Type: cross Abstract: Existing sequential recommenders usually allocate a fixed computational budget to every item in a user's interaction history, regardless of the item's contribution to the…
- arXiv cs.AIresearchGenerative Artificial Intelligence for Internet of Things Computing: A Systematic Survey
arXiv:2504.07635v2 Announce Type: replace Abstract: The integration of Generative Artificial Intelligence (GenAI) within the Internet of Things (IoT) is garnering considerable interest. This growing attention stems from…
- arXiv cs.AIresearchPRISM: Prompt-Refined In-Context System Modelling for Financial Retrieval
arXiv:2511.14130v3 Announce Type: replace Abstract: Deploying LLMs for financial information retrieval demands accuracy, cost efficiency, and reproducibility, yet most approaches require expensive fine-tuning. PRISM is a…
- arXiv cs.AIresearchSynthesis and Verification of Register Automata over Data Sequences (Technical Report)
arXiv:2511.19100v2 Announce Type: replace Abstract: Data sequences, such as time series, traces, and sensor streams, are words over large or infinite data domains. Their properties often depend on comparisons between val…
- arXiv cs.AIresearchA Theoretical Analysis of Why Masked Diffusion Models Mitigate the Reversal Curse
arXiv:2602.02133v3 Announce Type: replace Abstract: Autoregressive language models (ARMs) suffer from the reversal curse: after learning ''$A$ is $B$,'' they often fail on the reverse query ''$B$ is $A$.'' Masked diffusi…
- arXiv cs.AIagents2-Step Agent: How a Bayesian Decision Maker Learns from AI-Decision Support
arXiv:2602.21889v4 Announce Type: replace Abstract: Prognostic predictions from ML models support human decision making in several fields, including high-stakes ones such as healthcare and the judiciary. Yet, we still la…
- arXiv cs.AIresearchThe Alien Space of Science: Sampling Coherent but Cognitively Unavailable Research Directions
arXiv:2603.01092v3 Announce Type: replace Abstract: Scientific discovery is constrained not only by what is true, but by what is cognitively available to researchers. Many directions are coherent in light of the literatu…
- arXiv cs.AIresearchFactReview: Evidence-Grounded Peer Review with Execution-Based Claim Verification
arXiv:2604.04074v5 Announce Type: replace Abstract: AI is accelerating scientific research, but providing timely and trustworthy feedback remains challenging. Such feedback requires evaluating specific claims against ver…
- arXiv cs.AIresearchAdaptive auditing of AI systems with anytime-valid guarantees
arXiv:2605.07002v2 Announce Type: replace Abstract: A major bottleneck in characterizing the failure modes of generative AI systems is the cost and time of annotation and evaluation. Consequently, adaptive testing paradi…
- arXiv cs.AIresearchWindINR: Latent-State INR for Fast Local Wind Query and Correction in Complex Terrain
arXiv:2605.09511v2 Announce Type: replace Abstract: Many decisions in complex terrain require fast wind estimates at specific locations and heights rather than dense forecasts on fixed grids. We present WindINR, a latent…
- arXiv cs.AIresearchProposals and challenges for zero-knowledge verification of frontier AI training
arXiv:2606.05433v3 Announce Type: replace Abstract: Proposals for Frontier AI rules often use cumulative training compute as the main criterion for identifying high-impact models. Recent governance analyses call zero-kno…
- arXiv cs.AIagentsLean4Agent: Formal Modeling and Verification for Agent Workflow and Trajectory
arXiv:2606.06523v3 Announce Type: replace Abstract: Equipping Large Language Models (LLMs) to execute reliable multi-step workflows has become a central challenge in artificial intelligence. Despite recent advances in LL…
- arXiv cs.AIresearchRethinking Synthetic Rationale Supervision for Disease Prediction
arXiv:2606.10279v2 Announce Type: replace Abstract: Supervised fine-tuning with synthetic rationales is widely assumed to improve clinical prediction by teaching models not only what to predict but why. We test this assu…
- arXiv cs.AIresearchTNODEV: Toolbox for Neural ODE Verification
arXiv:2606.16567v3 Announce Type: replace Abstract: Neural ordinary differential equations (neural ODE) gained attention in safety critical settings such as continuous-time controllers for cyber-physical systems and clas…
- arXiv cs.AIresearchBeyond Accuracy: Measuring Logical Compliance of Predictive Models
arXiv:2606.20208v2 Announce Type: replace Abstract: Machine learning models are predominantly evaluated through predictive performance metrics such as ranking quality, prediction error, or classification accuracy. While…
- arXiv cs.AIresearchDeepLook: Uncertainty-Guided Branching with Lookahead Confidence for Efficient Reasoning
arXiv:2607.22602v2 Announce Type: replace Abstract: Test-time scaling improves large language model reasoning, but spends compute uniformly along a reasoning trace, even in stretches that are already stable. Motivated by…
- arXiv cs.AIagentsCTBench: Evaluating Troubleshooting Capabilities of AI Agents in Realistic Telecom Network Operations
arXiv:2608.12002v2 Announce Type: replace Abstract: While autonomous agents show promise for network operations, existing telecom troubleshooting evaluations fail to capture the partial observability and heterogeneity of…
- arXiv cs.AIresearchLanguage Models Give Group-Linked Fields the Wrong Weight
arXiv:2608.22887v2 Announce Type: replace Abstract: Large language models are increasingly used to judge people, and their answers can depend on inputs correlated with a protected attribute such as race, even when that a…
- arXiv cs.AIagentsFrom Atomic to Agentic: Towards Interpretable Evaluation of LLMs' Agentic Mathematical Capabilities
arXiv:2608.26950v2 Announce Type: replace Abstract: Large Language Models (LLMs) are evolving from performing end-to-end mathematical reasoning to integrating agentic intelligence. However, most existing math benchmarks…
- arXiv cs.AIresearchInterface-Induced Trajectory Censoring
arXiv:2609.03966v3 Announce Type: replace Abstract: Tool-use evaluations typically measure calls through a serving stack, which can report zero calls even when model outputs contain call-shaped payloads. We study interfa…
- arXiv cs.AIresearchWorld Models Under Asynchronous Sensor Observations
arXiv:2609.07299v2 Announce Type: replace Abstract: Learned world models typically assume that observations arrive synchronously, an abstraction inherited from simulators that return a complete state vector at each envir…
- arXiv cs.AIresearchAdaptive Entangled Neurons in the Brain and Natural Intelligence Simulations for Artificial General Intelligence
arXiv:2609.09226v2 Announce Type: replace Abstract: Understanding the brain's mechanisms is crucial in neuroscience, particularly for simulating natural intelligence and advancing artificial general intelligence (AGI). L…
- arXiv cs.AIresearchAI-driven Prices for Externalities and Sustainability in Production Markets
arXiv:2106.06060v5 Announce Type: replace-cross Abstract: Traditional competitive markets do not account for negative externalities; indirect costs that some participants impose on others, such as the cost of over-approp…
- arXiv cs.AIagentsAllocating Indivisible Goods to Strategic Agents: Pure Nash Equilibria and Fairness
arXiv:2109.08644v3 Announce Type: replace-cross Abstract: We consider the problem of fairly allocating a set of indivisible goods to a set of strategic agents with additive valuation functions. We assume no monetary tran…
- arXiv cs.AIresearchOptimized Conformal Selection: Powerful Selective Inference After Data-Dependent Score Construction
arXiv:2411.17983v2 Announce Type: replace-cross Abstract: We study data-driven score construction in conformal selection, which uses conformal p-values and multiple testing to select "interesting" instances with large un…
- arXiv cs.AIresearchAnnoCaseLaw: Legal Annotation Benchmark of Civil Negligence
arXiv:2503.00128v2 Announce Type: replace-cross Abstract: Language models offer a way to scale costly, labour-intensive expert annotation, but their ability to reproduce expert judgements remains uncertain. This is parti…
- arXiv cs.AIresearchQuantifying Symptom Causality in Clinical Decision Making: An Exploration Using CausaLM
arXiv:2503.19394v2 Announce Type: replace-cross Abstract: Current machine learning approaches to medical diagnosis often rely on correlational patterns between symptoms and diseases, risking misdiagnoses when symptoms ar…
- arXiv cs.AIresearchLearning to Interfere in Non-Orthogonal Multiple-Access Joint Source-Channel Coding
arXiv:2504.03690v2 Announce Type: replace-cross Abstract: We consider multiple transmitters aiming to communicate their source signals (e.g., images) over a multiple access channel (MAC). Conventional communication syste…
- arXiv cs.AIresearchQuantum-Inspired Optimization Process for Data Imputation
arXiv:2505.04841v3 Announce Type: replace-cross Abstract: Data imputation is a critical preprocessing step, particularly in clinical datasets where missingness can bias downstream inference. We propose a quantum-inspired…
- arXiv cs.AIagentsRemote Rowhammer Attack using Adversarial Observations on Federated Learning Clients
arXiv:2505.06335v3 Announce Type: replace-cross Abstract: Federated Learning (FL) has the potential for simultaneous global learning amongst a large number of parallel agents, enabling emerging AI systems to be trained a…
- arXiv cs.AIresearchEffects of Theory of Mind and Prosocial Beliefs on Steering Human-Aligned Behaviors of LLMs in Ultimatum Games
arXiv:2505.24255v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have shown potential in simulating human behaviors and performing theory-of-mind (ToM) reasoning, crucial for complex social interact…
- arXiv cs.AIresearchQuality-Diversity Red-Teaming: Automated Generation of High-Quality and Diverse Attackers for Large Language Models
arXiv:2506.07121v3 Announce Type: replace-cross Abstract: Ensuring the safety and robustness of large language models (LLMs) is a fundamental challenge and a critical prerequisite for the responsible deployment of artifi…
- arXiv cs.AIresearchSpatiotemporal Reconstruction of Global Radiation from Sparse Sensor Sequences
arXiv:2506.12045v3 Announce Type: replace-cross Abstract: Much of the physical world is inaccessible to direct measurement and can be reached only through the sparse traces it leaves in other observable variables. Here w…
- arXiv cs.AIagentsEnergy-Based Transfer for Reinforcement Learning
arXiv:2506.16590v2 Announce Type: replace-cross Abstract: Reinforcement learning remains sample inefficient, particularly when agents must adapt over time to related but distinct tasks, as in continual learning settings.…
- arXiv cs.AIresearchAgriVLN: Vision-and-Language Navigation for Agricultural Robots
arXiv:2508.07406v2 Announce Type: replace-cross Abstract: Agricultural robots are serving as powerful members across a wide range of tasks, nevertheless, they are still heavily relying on manual operations or railway sys…
- arXiv cs.AIresearchUniPrompt-CL: Efficient Single-Inference Continual Learning for Medical Image Classification
arXiv:2508.10954v3 Announce Type: replace-cross Abstract: Medical AI systems must remain reliable as data distributions shift across hospitals, imaging devices, and patient populations, yet privacy constraints often prev…